GAM740 Week 10:02 User Research – Carry out usability Research

Questions:

  • What do I want the user to be able to accomplish? 
  • Is there a part of the POD app that doesn’t work very well in regards to UX? 
  • Can the user be encouraged to identify areas that can be improved? 
  • Can Quantitative data be generated in this process? 
  • How can I ensure my research is reliable ?
  • How can I ensure my research is valid ? 

Jakob Nielsen (Nielsen Norman Group)

  • Do realistic tasks to reveal functions that may not be working. 
  • Give one task as a time. 
  • Tester can sit back away from the user
  • The user can ask questions but cannot answer it (can the user find the answer themselves). 
  • Encourage the user to think out loud. 
  • Tester can take notes.

Usertesting.com

  • User may not be screened correctly or be in the test group. 
  • It is better to observe directly if possible. 

Useit.com Blog

NN/g UX Conference address 

  • Reliability (can the test be repeated)
  • Validity (do the findings mean anything for the real world) 
Reflection:

When I worked for the oxford university press I engaged in usability testing as a user and gained insight into how that data can be captured and analysed in practice. They used hot spot tracking to capture where the user tracked their mouse, how long some decisions took to make and whether they were successful in using the software. 

When I researched at Loughborough University researchers used eye tracking detection technology to capture the gaze of the user. 

I could have access to the students of the Northamptonshire Podiatry school to test my app (subject to obtaining permission). As my app is aimed at student podiatrists, senior nurse practitioners and GP’s this would test a specific user group in the study. I am unsure whether I will be able to provide a working VR prototype at this time as interaction might be limited without the use of a hololens but I may be able to design a brochure app of the initial screens in Adobe XD which could provide some useful data. 

 

   

GAM740 Week 10:02 User Research – Questionnaire Design

Questionnaire Design

  • Consent Form 
– Thank you for taking part 
– Aim of Study 
  • Ask for 
– Gender (f/m) 
– Exact Age (not age categories) 
  • Radio Buttons 
Android
ios
  • Tick Boxes 
Multiple choice option
  • Phrase questions simply
  • Do not use Free Text Boxes (if possible) Use interview instead
  • Use rating scale questions (avoid 5 point scales)  likert scales
Example: Casual Gaming questionnaire
  • Organise the data in an EXCEL spreadsheet. 
  • Use excel functions to calculate 

– AVERAGE this will give you the mean 

– STDEV will give you Standard Deviation

These could form some of the questions on my questionnaire 

Sliding Questions 
How much do you enjoy the look and feel of this app ? 
How easy did you find it to navigate around the app? 
How easily could you pick up the instruments in the virtual clinic?
How useful did you find the clinic test ? 

Radio buttons
Do you use a tablet, a mobile phone or a desktop pc or mac? 
Do you use an ios device, android mobile device or do you not own a mobile device? 
Are you a GP, a Nurse Practitioner or Student ?   
Do you use any other mobile apps to help you with your podiatry studies ? yes/no
Do you use an anatomy mobile app ? yes/no
Are you female or male ? f/m
Would you use the app again?  yes/no
Do you generally access training on your mobile device or desktop computer ?  


GAM740 Week 10:01 User Research

Notes

Quantitative Research: Erik Geelhoed

  • Questionnaires : Online forums that collect data from users
  • Technology Logs: When an app was started ended, what buttons were selected 
  • Observations When, How Long, How Often 

Quantitative Research

1.Nominal Data (Frequency) Data

eg: Tally Red or yellow cars in an hour.
25 red cars and 10 yellow cars
Is this statistically significantly different?

 

2. Ordinal Data – Prioritise “what do you like better apples, bananas or clementines 
Forced to make a choice and order them.
This is the 3 alternative forced choice (3 AFC)

Statistical Analysis – a great many
Differences
Correlations which can lead to a market segmentation.

We do not know how much one option is preferred over the other choice.

3. Scale Data – ratings “where 1 is low and 5 is high” 
Scale data gives the most powerful statistical analysis.
T Test
Analysis of Variance (ANOVA)
Evaluate more than one variable at a time
Between subjects
within subjects

Similarities
correlations between two variables
cluster analysis/market segmentation

Independent variables
eg: female/male
Dependant variable
Eg: A question in a questionnaire. 

Descriptive Statistics

  •  Central Tendency

Mean (average value,scale data) What is the spread ?
Median (The 50% mark, scale and ordinal data)

  • Measures of spread 

standard deviation – used most often 


Mau (in the middle) 
Sigma (standard deviation)
either side is normal…. 

  • Normal Distribution  (Gaussian Distribution)

We are looking for a significant of 5% or less which indicates the level of confidence we can have in that the result is significant and not by chance.

p value of 0.05 

Other terms
Interval Data
Parametric Data
Non Parametric Date

Carry out Usability Research

Nielson Norman Group ” UX Training, Consulting & Research.

UX research informs interface design. So for example, if the designer needs to design an interface which young children will use then using a font size of 14 is generally considered to be the optimum font size. This is informed by quantitative research in which the user is observed and different font sizes tested.

In order to keep up with current discourse in UX Design research I follow key theorists and designers in the field. This includes the Nielson Norman group. 

Refs:

Observing the User Experience: A practitioners Guide to User Research  E Goodman

Measuring Customer satisfaction and loyalty: Survey Design, Use and Statistical analysis methods B.Hayes